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All Journal Jupiter Jurnal Media Infotama Syntax Jurnal Informatika Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding International Conference on Information Technology and Business Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan International conference on Information Technology and Business (ICITB) Journal of Animation & Games Studies Format : Jurnal Imiah Teknik Informatika Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer INTEGER: Journal of Information Technology JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Antivirus : Jurnal Ilmiah Teknik Informatika Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech Journal of Appropriate Technology for Community Services ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Ihsan: Jurnal Pengabdian Masyarakat Jifosi Nusantara Science and Technology Proceedings Jurnal Ilmiah Teknologi Informasi dan Robotika Jurnal Nasional Pengabdian Masyarakat International Journal of Data Science, Engineering, and Analytics (IJDASEA) Unram Journal of Community Service (UJCS) KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Jurnal Minfo Polgan (JMP) Literasi Nusantara Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Sistem Informasi dan Ilmu Komputer Jurnal Informatika Polinema (JIP) Repeater: Publikasi Teknik Informatika dan Jaringan International Journal of Information Engineering and Science Jurnal Riset Multidisiplin Edukasi Jurnal Sistem Informasi dan Ilmu Komputer Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Fetty Tri Anggraeny
Informatics, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294 Indonesia
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Kidney Stone Disease Diagnosis Using Shifted-Windows Transformer (SWIN Transformer): Diagnosis Penyakit Batu Ginjal Menggunakan Shifted-Windows Transformer (SWIN Transformer) Alfian Bima Prastyo; Fetty Tri Anggraeny; Retno Mumpuni
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol. 9 No. 4 (2025): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : DPD Jatim Perkumpulan Dosen Indonesia Semesta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kidney stones are a prevalent urological condition that, if undiagnosed that can lead to serious complications. Traditional diagnostic methods, such as manual ultrasound interpretation, are error-prone and time consuming, especially in areas with limited access to healthcare professionals. This research proposes the use of the Shifted Windows Transformer (Swin Transformer), a state-of-the-art deep learning model, to improve the classification of kidney stones in ultrasound images. The model is trained on a dataset of 9,396 kidney ultrasound images, categorized into two classes normal kidneys and kidneys with stones, sourced from a publicly available on Mendeley data Kidney dataset. The results demonstrate that the Swin Transformer achieves an impressive accuracy of 99.57%, surpassing others models like Convolutional Neural Networks (CNN) and Vision Transformers (ViT) by efficiently capturing both local and global features in high-resolution images. Practical implications include faster, more accurate diagnoses, particularly in regions lacking specialized radiologists. However, limitations of this model include its dependence on high-quality ultrasound images, which may not always be available in less-resourced settings. Additionally, the model’s performance may vary depending on the diversity of the dataset, limiting its generalizability in certain clinical environments. The need for substantial computational resources may also restrict the model's applicability in some healthcare settings. Despite these limitations, the Swin Transformer shows great promise as an automated tool for kidney stone detection, offering potential solutions for early diagnosis in remote and underdeveloped areas.
Perbandingan Algoritma Random Forest dan Logistic Regression Untuk Analisis Sentimen Ulasan Aplikasi Tumbuh Kembang Anak Di Play Store Muhammad Alfyando; Fetty Tri Anggraeny; Andreas Nugroho Sihananto
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 2 No. 1 (2024): Februari : Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v2i1.2262

Abstract

Early childhood plays an important role in forming the basis of development, which involves stimulation of various aspects such as moral religious values, social emotional, language, cognitive, and physical motor skills. The concept of early childhood learning is focused on play, where every activity is designed to be play, so that learning becomes more effective. Parents also need to understand today's children's education to interact with children positively. This research focuses on sentiment analysis of children's education-based app reviews on the Google Play Store, using Random Forest and Logistic Regression methods. The review data is taken from three apps with the theme of child development, namely "About Kids", "PrimaKu", and "Teman Bumil", with a range of review years between 2018 and 2023. The test results show that Logistic Regression has higher accuracy compared to Random Forest, especially in the "About Kids" and "PrimaKu" applications with accuracy above 90%. The conclusion of this research highlights the importance of sentiment analysis in improving understanding of user responses to children's education applications, with suggestions for future research to increase the number of datasets and variations in testing schemes by tuning hyperparameters to improve prediction accuracy and more optimal results.
Image Color Correction for Color Vision Deficiency Using ResNet and CycleGAN Adelia Putri Adyani; Fetty Tri Anggraeny; Eva Yulia Puspaningrum
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2506

Abstract

Color blindness is a visual impairment that limits an individual's ability to accurately perceive certain colors, particularly red, green, or blue. This condition can hinder daily tasks, especially when color identification is crucial. This study proposes a color correction system designed to enhance color perception for individuals with color vision deficiency (CVD), focusing on important visual areas within an image. The method involves converting RGB images into LMS color space, simulating types of color blindness (protanopia, deuteranopia, and tritanopia), detecting visually important regions using a saliency mask, applying color correction through a ResNet-based deep learning model, and performing a reverse transformation back to RGB using a CycleGAN. A total of 5,020 images were used for evaluation, and the proposed system achieved an average Root Mean Square (RMS) error of 0.0212. The Mean Absolute Error (MAE) ranged from 0.1541 to 0.5582 depending on the CVD type. In addition to quantitative evaluation, qualitative validation was conducted through a GUI-based user test involving 10 color blind participants. The system showed the highest effectiveness for deuteranopia with a color recognition accuracy of 71.666%, followed by tritanopia at 59.666% and protanopia at 46.500%. These results indicate that the proposed system offers significant potential in aiding individuals with CVD to better interpret color-based information, especially in visually important regions of an image. Future work may explore broader datasets and alternative deep learning architectures to further improve accuracy and adaptability.
Optimizing the ResNet50 Model with Five Optimizers for Detecting Rice Leaf Diseases Muchammad Syamsu Huda; Henni Endah Wahanani; Fetty Tri Anggraeny
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3232

Abstract

Rice (Oryza sativa) productivity is frequently threatened by foliar diseases such as Bacterial Leaf Blight, Brown Spot, Blast, and Tungro, which are often visually indistinguishable. This study achieved a high classification accuracy of 97.05% in detecting these diseases by optimizing the ResNet50 architecture with five optimizers Adam, Nadam, Adamax, RMSprop, and SGD and identifying Adamax as the most effective. Using transfer learning with ImageNet weights and data augmentation, the model was trained and validated on 4,400 labeled images from Kaggle, partitioned in a 70:20:10 ratio for training, validation, and testing. The methodological framework integrates three layers of innovation: (1) optimizing a deep residual CNN with comparative adaptive and non-adaptive optimizers; (2) employing transfer learning to accelerate convergence and reduce overfitting; and (3) deploying the best-performing model into an Android-based mobile application for real-time field detection. Results demonstrate that adaptive optimizers substantially enhance ResNet50’s learning stability and generalization compared to traditional methods. The Adamax variant exhibited the most stable convergence and minimal validation loss, proving effective for fine-grained visual differentiation between similar disease patterns. This research advances the current state-of-the-art in agricultural image classification by providing a systematic optimizer evaluation within a CNN transfer learning framework and extending its practical usability through mobile deployment. Future studies should address model compression, real-time inference optimization, and cross-crop generalization to strengthen the scalability of AI-assisted disease diagnosis in precision agriculture.
Optimasi Arsitektur DETR dan Augmentasi On-the-Fly untuk Deteksi Alfabet BISINDO Mukhamad Aziz Firmansyah; Fetty Tri Anggraeny; Yisti Vita Via
Jurnal Informatika Polinema Vol. 12 No. 3 (2026): Vol. 12 No. 3 (2026)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v12i3.9565

Abstract

Penelitian ini membahas penerapan Detection Transformer (DETR) yang dioptimasi dan dikombinasikan dengan augmentasi data on-the-fly untuk mendeteksi alfabet Bahasa Isyarat Indonesia (BISINDO) pada skenario real-time. Eksperimen dilakukan menggunakan dataset alfabet BISINDO yang terdiri dari 1.300 data dari 26 kelas (A–Z) dengan anotasi bounding box yang dikumpulkan secara mandiri pada lingkungan indoor terkontrol. Optimasi DETR dilakukan dengan menyederhanakan arsitektur dari konfigurasi standar menjadi 1 layer transformer encoder, 1 layer transformer decoder, dan 25 object queries untuk meningkatkan efisiensi inferensi. Augmentasi data on-the-fly diterapkan pada tahap pelatihan untuk memperkaya variasi data. Evaluasi dilakukan menggunakan metrik berbasis COCO menggunakan pustaka PyCOCOtools pada data uji serta pengujian real-time menggunakan input video frame-per-frame dari kamera. Hasil pengujian menunjukkan bahwa model tanpa augmentasi memperoleh nilai AP50 sebesar 0,987 pada data uji, sedangkan model dengan augmentasi on-the-fly memperoleh nilai AP50 sebesar 0,848. Meskipun nilai AP50 pada evaluasi statis menurun, hasil pengujian real-time menunjukkan bahwa model dengan augmentasi data on-the-fly menghasilkan deteksi yang lebih sesuai dibandingkan model tanpa augmentasi. Selain itu, optimasi arsitektur menghasilkan kecepatan inferensi hingga 58 FPS pada GPU dan 8 FPS pada CPU. Hasil ini menunjukkan bahwa penerapan DETR yang dioptimasi dan augmentasi data on-the-fly dapat digunakan untuk mendeteksi alfabet BISINDO pada skenario real-time.
Prediksi Tingkat Pengangguran di Wilayah Provinsi Jawa Timur Menggunakan Metode Elman Recurrent Neural Network (ERNN) Tompo Panjaitan; Fetty Tri Anggraeny; Muhammad Muharrom Al Haromainy
Jurnal Media Infotama Vol 20 No 2 (2024): Oktober
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v20i2.6198

Abstract

Tingkat pengangguran merupakan indikator penting dalam mengukur kesehatan ekonomi suatu negara dan kesejahteraan masyarakatnya. Dalam konteks Indonesia, masalah pengangguran terbuka menjadi tantangan yang perlu ditangani secara serius untuk mencapai pertumbuhan ekonomi yang inklusif dan berkelanjutan. Oleh karena itu dilakukan penelitian penerapan metode Elman Recurrent Neural Network untuk prediksi tingkat Pengangguran di wilayah Provinsi Jawa Timur. Data yang digunakan yaitu data Tingkat Pengangguran Terbuka (TPT) Provinsi Jawa Timur dari tahun 2001 sampai dengan tahun 2022. Data tersebut dirubah kedalam bentuk data time series dengan variabel berjumlah 5. Penelitian ini menggunakan jumlah epoch 500, learning rate (α) 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,0.9 dan 0.1, dan toleransi error 0.001. Hasil pengujian MSE pada penelitian ini menunjukkan nilai MSE terkecil pada learning rate 0.01 dengan data latih 90% dan data uji 10% dengan nilai MSE 0,061227.
IMPLEMENTASI HYBRID MODEL CEEMDAN-ARIMA-LSTM PREDIKSI HARGA SAHAM PENUTUP Dafauzan Bilal Syaifulloh; Fetty Tri Anggraeny; Achmad Junaidi
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 1 (2026): EDISI 27
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i1.6938

Abstract

Pergerakan harga saham yang bersifat non-linear dan non-stasioner menjadi tantangan utama dalam proses peramalan deret waktu. Penelitian ini mengusulkan model hybrid CEEMDAN–ARIMA–LSTM untuk meningkatkan akurasi prediksi harga penutupan saham PT Industri Jamu dan Farmasi Sido Muncul Tbk (SIDO). Metode CEEMDAN digunakan untuk mendekomposisi data saham menjadi beberapa Intrinsic Mode Functions (IMF), yang selanjutnya dianalisis menggunakan Sample Entropy (SampEn) guna mengidentifikasi tingkat kompleksitas dan menentukan model yang paling sesuai. Komponen dengan karakteristik linier diprediksi menggunakan ARIMA, sedangkan komponen non-linier dimodelkan menggunakan LSTM. Hasil prediksi dari seluruh IMF kemudian direkonstruksi menjadi nilai akhir. Evaluasi kinerja menggunakan MAPE, MAE, RMSE, dan R² menunjukkan bahwa model hybrid memberikan akurasi yang lebih tinggi dibandingkan model tunggal, dengan nilai MAPE yang termasuk dalam kategori sangat akurat. Temuan ini menegaskan bahwa integrasi CEEMDAN dengan pendekatan statistik dan deep learning mampu menangani dinamika kompleks pada data saham serta meningkatkan kualitas prediksi secara signifikan.
PENERAPAN CONTENT BASED FILTERING DAN HDBSCAN UNTUK REKOMENDASI DEVELOPER Muhmmad Fairus Ramadhani; Eva Yulia Puspaningrum; Fetty Tri Anggraeny
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 1 (2026): EDISI 27
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i1.7193

Abstract

Penentuan developer yang tepat berdasarkan log aktivitas berupa teks tidak terstruktur merupakan tantangan penting dalam manajemen penugasan proyek. Penelitian-penelitian sebelumnya umumnya menggunakan Content-Based Filtering atau algoritma clustering konvensional seperti K-Means dan DBSCAN, yang memiliki keterbatasan dalam menangani data aktivitas developer yang padat. Penelitian ini mengusulkan pendekatan sistem rekomendasi yang mengintegrasikan Content-Based Filtering (CBF) dengan clustering berbasis kepadatan menggunakan HDBSCAN, yang  belum pernah diterapkan secara langsung dalam konteks rekomendasi developer berbasis log aktivitas. Log aktivitas direpresentasikan menggunakan TF-IDF dan direduksi dimensinya dengan UMAP, kemudian dikelompokkan menggunakan HDBSCAN tanpa memerlukan penentuan jumlah klaster di awal, sehingga lebih efektif dalam mengelola data aktivitas yang padat. Rekomendasi dihasilkan berdasarkan kedekatan jarak dalam klaster yang terbentuk. Evaluasi pada dataset yang terdiri dari 4.505 log aktivitas dan 45 data uji menunjukkan bahwa konfigurasi parameter min_cluster_size = 2 dan min_samples = 2 menghasilkan performa terbaik dengan nilai Hit Ratio sebesar 88,8%, Recall sebesar 80%, dan Mean Reciprocal Rank (MRR) sebesar 56,3%. Dibandingkan dengan pendekatan Content-Based Filtering saja, metode yang diusulkan menunjukkan peningkatan pada Hit Ratio dan Recall, yang mengindikasikan peningkatan cakupan dan relevansi rekomendasi developer.
The Effect of Contrast Enhancement on Retinal Blood Vessel Segmentation Using CAS-UNet with Coordinate Attention Mardhatilla Al Haadiy, Hilya Zada; Anggraeny, Fetty Tri; Puspaningrum, Eva Yulia
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 21, No 1 (2026): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v21i1.26937

Abstract

Low contrast variation, uneven intensity distribution, and the presence of noise in retinal fundus images pose major challenges for blood vessel segmentation, particularly regarding thin and complex structures. These conditions make it difficult for models to accurately distinguish between blood vessels and the background. This study aims to analyze the impact of contrast enhancement techniques on retinal blood vessel segmentation performance using a CAS-UNet architecture modified with Coordinate Attention (CA). The methodology involves three preprocessing scenarios: Grayscale, Grayscale + CLAHE, and Grayscale + CLAHE + Gamma Correction. The model was trained using the DRIVE and CHASE_DB1 datasets with an 80:20 data split, an SGD optimizer, a learning rate of 0.01, and a combined BCE and Dice loss function over 50 epochs. Evaluation was conducted using a confusion matrix based on accuracy, sensitivity, specificity, F1-score, and IoU metrics. The results indicate that the Grayscale + CLAHE combination yielded the best performance—achieving a sensitivity of 81.46%, an F1-score of 81.63%, and an IoU of 69.01%—while also improving the detection of small blood vessels more consistently. These findings demonstrate that the appropriate application of contrast enhancement plays a crucial role in improving the quality of medical image segmentation.
Pengembangan Sistem Pendukung Keputusan Manajemen Risiko Logistik E-Commerce Berbasis Machine Learning Menggunakan Random Forest Pipeline Fetty Tri Anggraeny; Naufal Firman Dhani
Jurnal Riset Multidisiplin Edukasi Vol. 3 No. 6 (2026): Jurnal Riset Multidisiplin Edukasi (Juni 2026)
Publisher : PT. Hasba Edukasi Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71282/jurmie.v3i6.2259

Abstract

The e-commerce logistics industry faces challenges due to the high risk of fleet delivery delays, where the analysis of risk mitigation and its financial impact is often limited for retail operational management. This project aims to develop an Integrated Logistics Risk Management Decision Support System Smart Platform named LogiTrack v2.0, which combines two main data engineering domains: Data Mining and Machine Learning. For data analysis, an artificial intelligence model was developed using the Random Forest Classifier algorithm within a Scikit-Learn Pipeline architecture, which detects minority class data via the class_weight='balanced' parameter to automatically predict cargo delay status based on the engineering of four composite features (Volume, Density, Shipping Fee per Gram, and Route). For smart interaction, binary file .pkl optimization was implemented using the Joblib library with compression level 3 to radically reduce storage capacity from 138.80 MB to 28.40 MB. All of these functionalities are integrated into a single interactive web application built using Streamlit and deployed on the Streamlit Cloud server. The dashboard is equipped with two main tools: a Guardrail Engine function to block package inputs exceeding retail capacity ($>30$ Kg) and a Business Impact Calculator panel to calculate the conversion of management financial penalties by 20% in real-time. Functional testing results via Black-Box Testing show that the classification model achieves a global accuracy rate of 90.00% and a precision value of 0.31, while the web platform successfully operates stably with low latency. This platform offers a holistic solution that enhances cargo distribution transparency, optimizes operational risk control efficiency, and suppresses potential financial losses within the logistics industry ecosystem.
Co-Authors Abdul Aziz Naufal Farizqi Abu Musa, Hammam Bara Ach.Diki Prasetyo, Ach.Diki Prasetyo Achmad Junaidi Adelia Putri Adyani Afina Lina Nurlaili Agung Mustika Rizki Agung Mustika Rizki, Agung Mustika Agussalim, Agussalim Ahmad Sofian Aris S Akbar, Fawwaz Ali Akbar, Iqbal Imani Khoirul Alfian Bima Prastyo Alfiani, Fina Alibasyah, Fahmi Nugroho Alviriza Ramadhan, Muhammad Amalia, Nadhia Rizqy Anabella, Linda Happy Andreas Nugroho Sihananto Andreas Nugroho Sihananto Aprillian, Farrel Archamul Fajar Pratama Ariadi, Kuncoro Atmojo, Unggul Widi Ayu Puspita, Nabila Ayuningrum, Agnes Athalia Azira, Volem Alvaro Azizi, Abrar Bachtiar Riza Pratama Basuki Rahmat Basuki Rahmat Masdi Siduppa Cahyas, Jerry Ramadhani Dafauzan Bilal Syaifulloh Dedin F. Rosida Dedin Finatsiyatull Rosida Dianto, Alfian Rachmad Dimara, Denis Lizard Sambawo Dimas Saputra Dita Atasa Diyasa, I Gede Susrama Mas Dyan Agustin Dzulqornain, Muhammad Rif'an Erik evranata Pardede Eva Yulia Puspaningrum Evi Suryaningsih Fadillah, Mochamad Nor Fahmi Anugrah Danendra Faisal Muttaqin Farkhan, Farkhan Faturrahman Rahardjo, Iqbal Raihan Firjatullah, Adika Firza Prima Aditiawan Fitria Eka Wulandari Fitriansyah, Muhammad Daffa Gideon Setya Budiwitjaksono Habibi, Faisal Wildan Hadi, I Putu Mahardika Cahyana Handono, Stevanus Frangky Handoyo Prasetyo Hartanti, Syafrida Maulina Hasan, Ferry Hasby Bik, Ahmad Hasya, Astrini Hadina Hatta, Heliza Rahmania Henni Endah Wahanani Hilal, Muhammad Hsya, Astrini Hadina I Gede Susrama Masdiyasa Intan Yuniar Purbasari Intan Yuniar Purbasari, Intan Yuniar Irawan, Nauval Maulana Rizky Isworo, Muhamad Raihan Ramadhani Julianto Dwi Putra, Rico Khairil Amin, Mohammad Khonsa Salsabila Kusuma, Nugraha Varrel Made Hanindia Prami Swari Mahardika Virgo Wuryantoro Manalu, Daniel Mardhatilla Al Haadiy, Hilya Zada Maulana, Hendra Maulana, Rafie Ishaq Meike Hardianti Merdin Risalul Abrori Mochamad Nor Fadillah Mohamad Ilham Prasetyo Raharjo Mohammad Idhom Monica Widiasri, Monica Muchammad Syamsu Huda Muhammad Ahsanur Rafi Muhammad Alfin Jimly Asshiddiqie Muhammad Alfyando Muhammad Dawam Fakhri Muhammad Muharrom Al Haromainy Muhmmad Fairus Ramadhani Mukhamad Aziz Firmansyah Munoto Mustika Rizki, Agung Mutoffar, Muhamad Malik Naila, Amelia Maslaqun Nashrulloh, Muhammad Atay Nadhif Naufal Firman Dhani Nicholas, Sandy Novarina, Fitria Nugroho Sihananto, Andreas Nur Aini Ersanti Nurfiana Pradana, Marchel Adias Pradipta, M. Najmi Arya Prastya, Ade Fathoni Pratama Wirya Atmaja Pratama Wirya Atmaja Pratama, Muhammad Lutfi Pratiwi, Nisa Prihantono, Silvanus Putra, Chrystia Aji Putra, Riza Satria Putri, Shintyadhita Wirawan Radical Rakhman Rafie Ishaq Maulana Rama Andika Jorgie Rangga Widiasmara Rayhan Rizal Mahendra Retno Mumpuni Retno Mumpuni Reza Aminullah Ridho Aji Pangestu Ronggo Alit Salsabilah, Rafani Bardatus Sandy Rizkyando Sani, Yusmia Washiatus Sanjaya, Alvian Dwi Sankalla, Sabda Saputra, Rendi Cahya Satria, Ramadhan Dani Satria, Vinza Hedi Septyono, Muhammad Bagas Setianto, Christian Wahyu Shalehuddin Albawani, Raden Sholihuddin, Muhammad Thoriq Siagian, Pangestu Sandya Etniko Singgih Putra Pratama Singgih Putra Pratama Siregar, Talitha Aurora Nadenggan Sri Kuswayati Subairi Subairi Sugiarto Sukandar, Ivan Christopher Sulthan Ahmad Sunarko, Victor Immanuel Supangkat, Dwiki Aditama Suryaningsih, Evi Susanto, Adyatma Imam Suwito Suwito Syahrul Munir Syahrul Munir Syaifulloh, Dafauzan Bilal Tarsinah Sumarni Taufiqqurrahman, Husain Taufiqurrahman, Rahmadany Fahreza Thariq, Muhammad Fadli Al Titin Sumarni Tompo Panjaitan Trianingsih, Arini Vita Via, Yisti Wahyu S.J. Saputra Wardhani, Adil Sandy yisti vita via Yisti Vita Via Yisti Vita Via Yuniar Purbasari, Intan Zainal Abidin Achmad